Data Analytics in Julia
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By , PhD student in Computational Communication, NUS
(Generated by GPT-4o)
This short book provides a practical guide for data analysis in social science using Julia. It replicates common analytical steps in the field.
Because of its speed.
✅ Why do we need Julia
✅ How to install Julia
✅ How to install Julia as a Jupyter kernal for notebooks
✅ The basics of operations, data structures, packages, methods, and define functions
✅ Load a dataframe from a local file, an online link, and a common datasets; or create it from scratch
✅ Select by rows, columns, and conditions.
✅ Split and combine
✅ Grouping
✅ Sorting
✅ Transforming between long / wide tables
✅ Find / fill / drop missing values
✅ Data pipeline
✅ Manipulate strings
✅ Network
✅ Common parametric tests (t-tests and ANOVA)
✅ Regression (multi-variate regression and fixed effects models)
✅ Path Analysis
✅ Mediation
✅ Moderation
✅ Conditional Path Analysis
🚧 / ✅ Counterfactual Framework
🚧 / ✅ Instrumental Variables
🚧 / ✅ Regression Discontinuity Design
🚧 / ✅ Difference-in-Difference
🚧 / ✅ Synthetic Control
🚧 / ✅ Synthetic Difference-in-Difference
(ggplot2-like)
✅ Scatterplot, barplot, lineplot, and histogram
✅ Styles and themes
✅ Multiple-plots in facets
✅ Using R functions and R code blocks in Julia
✅ Using Python functions and Python code blocks in Julia
✅ Tips for increasing the speed
✅ Profiling tool and visualization
✅ All codes used for plotting
This work is licensed under a.
